code: def encompass_shuffling_and_batch_filling(
      model_fn: Callable, batch_size: int
  ) -> Callable:
      """
      Takes a function that already handles batching but additionally performing shuffling
      and ensures instances are evaluated as part of full batches.
      """
  
      def new_model_fn(x: Union[np.ndarray, list]) -> np.ndarray:
          is_np = isinstance(x, np.ndarray)
          # shuffle
          n_x = len(x)
          perm = np.random.permutation(n_x)
          x = x[perm] if is_np else [x[i] for i in perm]
          # add extras if necessary
          final_batch_size = n_x % batch_size
          if final_batch_size != 0:
              doubles_inds = random.choices(
                  [i for i in range(n_x)],
                  k=batch_size - final_batch_size,
              )
              if is_np:
                  x = np.concatenate([x, x[doubles_inds]], axis=0)  # type: ignore[call-overload]
              else:
                  x += [x[i] for i in doubles_inds]
          # remove any extras and unshuffle
          preds = np.asarray(model_fn(x))[:n_x]
          preds = preds[np.argsort(perm)]
          return preds
  
      return new_model_fn
  
file_path: alibi-detect/alibi_detect/cd/utils.py
imports: import logging
  import random
  from typing import Callable, Dict, Optional, Tuple, Union
  import numpy
  from alibi_detect.utils.sampling import reservoir_sampling
  from alibi_detect.utils.frameworks import Framework
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 3 for calling a global function with example usage: encompass_shuffling_and_batch_filling(model_fn, batch_size) and returns: new_model_fn, preds